• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.259-278
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• 2022

With the spread of Artificial Intelligence (AI), various AI-based services are expanding in the financial sector such as service recommendation, automated customer response, fraud detection system(FDS), credit scoring services, etc. At the same time, problems related to reliability and unexpected social controversy are also occurring due to the nature of data-based machine learning. The need Based on this background, this study aimed to contribute to improving trust in AI-based financial services by proposing a checklist to secure fairness in AI-based credit scoring services which directly affects consumers' financial life. Among the key elements of trustworthy AI like transparency, safety, accountability, and fairness, fairness was selected as the subject of the study so that everyone could enjoy the benefits of automated algorithms from the perspective of inclusive finance without social discrimination. We divided the entire fairness related operation process into three areas like data, algorithms, and user areas through literature research. For each area, we constructed four detailed considerations for evaluation resulting in 12 checklists. The relative importance and priority of the categories were evaluated through the analytic hierarchy process (AHP). We use three different groups: financial field workers, artificial intelligence field workers, and general users which represent entire financial stakeholders. According to the importance of each stakeholder, three groups were classified and analyzed, and from a practical perspective, specific checks such as feasibility verification for using learning data and non-financial information and monitoring new inflow data were identified. Moreover, financial consumers in general were found to be highly considerate of the accuracy of result analysis and bias checks. We expect this result could contribute to the design and operation of fair AI-based financial services.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 1999.10a
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• pp.56-56
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• 1999

For advanced TFT-LCD manufacturing processes, dry etching of thin-film layers(a-Si, $SiN_x$, SID & gate electrodes, ITO etc.) is increasingly preferred instead of conventional wet etching processes. To dry etch Al gate electrode which is advantageous for reducing propagation delay time of scan signals, high etch rate, slope angle control, and etch uniformity are required. For the Al gate electrode, some metals such as Ti and Nd are added in Al to prevent hillocks during post-annealing processes in addition to gaining low-resistivity($<10u{\Omega}{\cdot}cm$), high performance to heat tolerance and corrosion tolerance of Al thin films. In the case of AI-Nd alloy films, however, low etch rate and poor selectivity over photoresist are remained as a problem. In this study, to enhance the etch rates together with etch uniformity of AI-Nd alloys, magnetized inductively coupled plasma(MICP) have been used instead of conventional ICP and the effects of various magnets and processes conditions have been studied. MICP was consisted of fourteen pairs of permanent magnets arranged along the inside of chamber wall and also a Helmholtz type axial electromagnets was located outside the chamber. Gas combinations of $Cl_2,{\;}BCl_3$, and HBr were used with pressures between 5mTorr and 30mTorr, rf-bias voltages from -50Vto -200V, and inductive powers from 400W to 800W. In the case of $Cl_2/BCl_3$ plasma chemistry, the etch rate of AI-Nd films and etch selectivity over photoresist increased with $BCl_3$ rich etch chemistries for both with and without the magnets. The highest etch rate of $1,000{\AA}/min$, however, could be obtained with the magnets(both the multi-dipole magnets and the electromagnets). Under an optimized electromagnetic strength, etch uniformity of less than 5% also could be obtained under the above conditions.

• Tuberculosis and Respiratory Diseases
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• v.84 no.2
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• pp.115-124
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• 2021

Background: This study aimed to determine the parameters for worsening oxygenation in non-severe coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective cohort study included cases of confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as that with SpO₂ ≤94% or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening oxygenation group that did not experience any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest computed tomography (CT) was calculated using artificial intelligence (AI) and measured visually by a radiologist. Results: We included 136 patients, with 32 (23.5%) patients in the worsening oxygenation group; of whom, two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups; however, univariate logistic regression analysis revealed that a variety of parameters on admission were associated with an increased risk of a desaturation event. A subset of patients was studied to eliminate potential bias, that ferritin ≥280 ㎍/L (p=0.029), lactate dehydrogenase ≥240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52). Conclusion: Our study suggests that initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation on admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.233-237
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• 2021

With the advent of the 4th industrial revolution, interest in artificial intelligence education is increasing in elementary schools. In order to nurture future talents with artificial intelligence capabilities, AI education should be actively conducted at school sites. Although basic software education is provided in the 2015 revised curriculum, there is a tendency to view the programming process that creates artificial intelligence only as a problem-solving process. However, when creating an artificial intelligence, the value of the developer who creates artificial intelligence is projected. Therefore, it is necessary to deal with the contents of artificial intelligence value judgment during SW education. This study has limitations due to the fact that Delphi research was conducted with a group of experts. In the future, it is judged that quantitative research should be conducted to supplement these limitations.

• JSTS:Journal of Semiconductor Technology and Science
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• v.8 no.2
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• pp.139-142
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• 2008

An X-band MMIC power amplifier for radar application is developed using $0.25-{\mu}m$ gate length GaAs pHEMT technology. A bus-bar power combiner at output stage is used to minimize the combiner size and to simplify bias network. The fabricated power amplifier shows 38.75 dBm (7.5 Watt) Psat at 10 GHz. The chip size is $3.5\;mm{\times}3.9\;mm$.

• Journal of the Korean Vacuum Society
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• v.2 no.4
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• pp.474-479
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• 1993

Submicron contact hole filling with aluminum alloys has been achieved with a multistep metallization method, which utilizes a metal " flow" or self-diffusion process at elevated temperatures after the metal was sputter-deposited. A multi-chamber, modular sputtering system was employed to deposit aluminum alloys and subsequently to anneal the deposited metal films under vacuum at high temperatures. The film were deposited on 200 mm wafers with planar, dc magnetron sputtering sources without anysubstrate bias. The basic process steps studied for the multistep metallization include an initial layer deposition at low temperatures less than $100^{\circ}C$, and an annealin gstep at elevated temperatures, between 450 and $550^{\circ}C$. The degree of planarization or step coverage was dependent strongly upon the temperature and time of the flow step and complete filling of the submicron contacts with aluminum alloys was achieved. Responsible mechanisms for the enhancement in step coverge and factros determining uniform and reproducible flow of aluminum alloys during the high temperauture step are discussed.discussed.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.401-418
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• 2023

Recently, the socio-economic interest in Large Language Models (LLMs) has been growing due to the emergence of ChatGPT. As a type of generative AI, LLMs have reached the level of script creation. In this regard, it is important to address the issue of discrimination (sexism, racism, religious discrimination, ageism, etc.) in the performing arts in general or in specific performing arts works or organizations in a large language model that will be widely used by the general public and professionals. However, there has not yet been a full-scale investigation and discussion on the issue of discrimination in the performing arts in large-scale language models. Therefore, the purpose of this study is to textually analyze the perceptions of discrimination issues in the performing arts from LMMs and to derive implications for the performing arts field and the development of LMMs. First, BBQ (Bias Benchmark for QA) questions and measures for nine discrimination issues were used to measure the sensitivity to discrimination of the giant language models, and the answers derived from the representative giant language models were verified by performing arts experts to see if there were any parts of the giant language models' misperceptions, and then the giant language models' perceptions of the ethics of discriminatory views in the performing arts field were analyzed through the content analysis method. As a result of the analysis, implications for the performing arts field and points to be noted in the development of large-scale linguistic models were derived and discussed.

• Korean Journal of Adult Nursing
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• v.25 no.4
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• pp.377-388
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• 2013

Purpose: The purpose of this study was to assess the effects of cognitive behavioral therapy (CBT) on depression, anxiety, self care behavior and quality of life in cancer patients. Methods: Two thousand and eighty three abstracts were identified through six electronic databases (1980 to June 2012) in Korea. Seventeen studies involving 679 participants met the inclusion criteria for meta analysis. Two authors independently assessed trial quality by Cochrane's Risk of Bias and Methodological Items for Non Randomized Studies and extracted data. The data were analyzed by the RevMan 5.2 program of Cochrane library. Results: Overall, study quality was moderate to high. CBT was conducted for a mean of 4.2 weeks, 7 sessions and an average of 36.1-minutes per session. CBT was effective for depression (d=-0.85; 95% CI=-1.09, -0.61), anxiety (d=-0.52; 95% CI=-0.75, -0.29), self care behavior (d=-1.34; 95% CI=-1.93, -0.74), and quality of life (d=-0.42; 95% CI=-0.80, -0.04). Publication bias was not detected as evaluated by funnel plot and Egger's test. Conclusion: CBT has small to large effects on depression, anxiety, self care and quality of life. These finding suggests that various CBT interventions can assist cancer patients in reducing emotional distress and improving self care and quality of life.

• Proceedings of the Korean Institute of Surface Engineering Conference
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• 2001.11a
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• pp.67-67
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• 2001

The structure zone model has been used to provide an overview of the relationship between the microstructure of the films deposited by PVD and the most prominent deposition condition.s. B.AMovchan and AV.Demchishin have proposed it firstls such model. They concluded that the general features of the resulting structures could be correlated into three zones depending on $T/T_m$. Here T m is the melting point of the coating material and T is the substrate temperature in kelvines. Zone 1 ($T/Tm_) is dominated by tapered macrograins with domed tops, zone 2 ($O.3) by columnar grains with denser boundaries and zone 3 ($T/T_m>O.5$) by equiaxed grains formed by recrystallization. J.AThomton has extended this model to include the effect of the sputtering gas pressure and found a fourth zone termed zone T(transition zone) consisting of a dense array of poorly defined fibrous grains. R.Messier found that the zone I-T boundary (fourth zone of Thorton) varies in a fashion similar to the film bias potential as a function of gas pressure. However, there has not nearly enough model for explaining the change in morphology with crystal orientation of the films. The structure zone model only provide an information about the morphology of the deposited film. In general, the nucleation and growth mechanism for granular and fine structure of the deposited films are very complex in an PVD technique because the morphology and orientation depend not only on the substrate temperature but also on the energy of deposition of the atoms or ions, the kinetic mechanism between metal atoms and argon or nitrogen gas, and even on the presence of impurities. In order to clarify these relationship, AI and Mg thin films were prepared on SPCC steel substrates by PVD techniques. The influence of gas pressures and bias voltages on their crystal orientation and morphology of the prepared films were investigated by SEM and XRD, respectively. And the effect of crystal orientation and morphology of the prepared films on corrosion resistance was estimated by measuring polarization curves in 3% NaCI solution.